We evaluate our approach on three standard few-shot segmentation datasets, PASCAL-5\(^{i}\)  [1]}, COCO-20\(^{i}\)  [2]}, and FSS-1000 [3]}. PASCAL-5\(^{i}\)  is created from images from PASCAL VOC 2012 [4]} and extra mask annotations [5]}, Originally, 20 object classes are available, but for cross-validation, as done in OSLM [1]}, they are evenly divided into 4 folds \(i \in \lbrace 0,1,2,3\rbrace \) , and this makes each fold contains 5 classes. COCO-20\(^{i}\)  contains 80 object classes, and as done for PASCAL-5\(^{i}\) , the dataset is evenly divided into 4 folds, which results 20 classes for each fold. FSS-1000 is a more diverse dataset consisting of 1000 object classes. Following [3]}, we divide 1000 categories into 3 splits for training, validation and testing, which consist of 520, 240 and 240 classes, respectively. For PASCAL-5\(^{i}\)  and COCO-20\(^{i}\) , we follow the common evaluation practice [8]}, [9]}, [10]} and standard cross-validation protocol; for each \(i\) -th fold, the target fold \(i\)  is used for evaluation and other folds are used for training.
